Systems and methods for machine learning based physiological motion measurement

ABSTRACT

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

TECHNICAL FIELD

The present disclosure generally relates to physiological motionmeasurement, and more particularly, methods and systems for measuring aphysiological motion of a region of interest (ROI) based on a machinelearning technique.

BACKGROUND

Medical imaging is widely used in disease diagnosis and/or treatment. Asubject, such as a patient, may be scanned by a medical imaging deviceto acquire image data of the subject for analysis. In some occasions, anROI of the subject may undergo a physiological motion (e.g., a cardiacmotion, a respiratory motion, etc.) during the scan and a plurality ofimages of the ROI corresponding to a plurality of motion phases may begenerated. The images of the ROI corresponding to different motionphases may be used to evaluate a physiological condition of the ROI. Forexample, by performing a magnetic resonance (MR) scan on the heart of apatient, a plurality of cardiac MR imaging (CMRI) cine images may beacquired for assessing the myocardial function of the patient. Thus, itis desirable to provide effective systems and methods for physiologicalmotion measurement, thereby improving the accuracy of disease diagnosisand/or treatment.

SUMMARY

According to one aspect of the present disclosure, a system forphysiological motion measurement is provided. The system may include atleast one storage device including a set of instructions forphysiological motion measurement, and at least one processor configuredto communicate with the at least one storage device. When executing theinstructions, the at least one processor may be configured to direct thesystem to perform the following operations. The at least one processormay be configured to direct the system to acquiring a reference image ofan ROI corresponding to a reference motion phase of the ROI and a targetimage of the ROI corresponding to a target motion phase of the ROI, thetarget motion phase being different from the reference motion phase. Theat least one processor may be also configured to direct the system toidentify one or more feature points relating to the ROI from thereference image. The at least one processor may be further configured todirect the system to determine a motion field of the one or more featurepoints from the reference motion phase to the target motion phase usinga motion prediction model, wherein an input of the motion predictionmodel includes at least the reference image and the target image. The atleast one processor may be further configured to direct the system todetermine a physiological condition of the ROI based on the motionfield.

In some embodiments, the ROI may include at least one of a heart, alung, an abdomen, a chest, a stomach, or of a subject.

In some embodiments, the ROI may be a heart, and the one or more featurepoints relating to the heart in the reference image may include an innerpoint on an endocardium of the heart and a corresponding outer point onan epicardium of the heart.

In some embodiments, to identify the inner point and the correspondingouter point from the reference image, the at least one processor may beconfigured to direct the system to segment the endocardium and theepicardium from the reference image, and identify the inner point andthe corresponding outer point from the reference image based on thepositions of the endocardium and the epicardium.

In some embodiments, the motion field may include one or more motionvectors corresponding to the one or more features points. To determine aphysiological condition of the heart, the at least one processor may beconfigured to direct the system to determine a first distance betweenthe inner point and the corresponding outer point in the referencemotion phase based on the reference image. The at least one processormay also be configured to direct the system to determine a seconddistance between the inner point and the corresponding outer point inthe target motion phase based on the motion vector of the inner pointand the motion vector of the outer point. The at least one processor mayfurther be configured to direct the system to determine a strain valuerelating to the heart based on the first distance and the seconddistance.

In some embodiments, the motion prediction model may be trainedaccording to a supervised training process. The supervised trainingprocess may include obtaining at least one annotated training sample.Each of the at least one annotated training sample may include a firstannotated image of a sample ROI corresponding to a first motion phase, asecond annotated image of the sample ROI corresponding to a secondmotion phase, and a sample motion field between the first annotatedimage and the second annotated image. The first annotated image may beannotated with one or more first sample feature points relating to thesample ROI, and the second annotated image may be annotated with one ormore second sample feature points corresponding to the first samplefeature points. The supervised training process may also includegenerating the motion prediction model by training a preliminary modelusing the at least one annotated training sample according to asupervised learning technique.

In some embodiments, the motion prediction model may be trainedaccording to an unsupervised training process. The unsupervised trainingprocess may include obtaining at least one training sample. Each of theat least one training sample may include a first image of a sample ROIin a first motion phase and a second image of the sample ROI in a secondmotion phase. The unsupervised training process may further includegenerating the motion prediction model by training a preliminary modelusing the at least one training sample according to an unsupervisedlearning technique.

In some embodiments, the preliminary model may be a generativeadversarial network (GAN) model.

According to one aspect of the present disclosure, a system forgenerating a motion prediction model is provided. The system may includeat least one storage device storing a set of instructions for generatinga motion prediction model, and at least one processor configured tocommunicate with the at least one storage device. When executing theinstructions, the at least one processor may be configured to direct thesystem to perform the following operations. The at least one processormay be configured to direct the system to obtain at least one trainingsample. Each training sample may include a first image and a secondimage indicative of a physiological motion of a sample ROI. The firstimage may correspond to a first motion phase of the sample ROI, and thesecond image may correspond to a second motion phase of the sample ROI.The at least one processor may also be configured to direct the systemto generate the motion prediction model by training a preliminary modelusing the at least one training sample according to an unsupervisedlearning technique.

In some embodiments, to generate the motion prediction model by traininga preliminary model, the at least one processor may be configured todirect the system to train the preliminary model by minimizing a lossfunction, and designate at least a portion of the trained preliminarymodel as the motion prediction model. The loss function may relate to adifference between the second image of the training sample and apredicted second image. The predicted second image may be generatedbased on the first image and the second image of the training sample andthe preliminary model.

In some embodiments, the training the preliminary model by minimizing aloss function may include an iterative operation including one or moreiterations. For each of the at least one training sample, at least oneiteration of the iterative operation may include generating a firstmotion field from the first image to the second image using an updatedpreliminary model determined in a previous iteration, and generating apredicted second image by warping the first image of the training sampleaccording to the first motion field. For each of the at least onetraining sample, the at least one iteration of the iterative operationmay also include determining a first difference between the predictedsecond image and the second image of the training sample, anddetermining a value of the loss function based at least in part on thefirst difference corresponding to each of the at least one trainingsample. For each of the at least one training sample, the at least oneiteration of the iterative operation may further include updating theupdated preliminary model to be used in a next iteration.

In some embodiments, for each of the at least one training sample, theat least one processor may be further configured to direct the system togenerate a second motion field from the second image to the first imageusing the preliminary model, and determine an opposite motion field ofthe second motion field. For each of the at least one training sample,the at least one processor may further be configured to direct thesystem to determine a second difference between the opposite motionfield and the first motion field of the training sample. The value ofthe loss function may be determined further based on the seconddifference corresponding to each training sample.

In some embodiments, for each of the at least one training sample, theat least one processor may be configured to direct the system togenerate a predicted first image by warping the second image of thetraining sample according to the first image of the training sampleusing the preliminary model. For each of the at least one trainingsample, the at least one processor may also be configured to direct thesystem to generate a third image by warping the predicted first imageaccording to the second image using the preliminary model, and generatea fourth image by warping the predicted second image according to thefirst image using the preliminary model. For each of the at least onetraining sample, the at least one processor may further be configured todirect the system to determine a third difference between the thirdimage and the second image and a fourth difference between the fourthimage and the first image. The value of the loss function may bedetermined further based on the third difference and the fourthdifference corresponding to each training sample.

In some embodiments, the preliminary model may include a generator. Foreach of the at least one training sample, the generator may beconfigured to predict a first motion field from the first image of thetraining sample to the second image of the training sample.

In some embodiments, the preliminary model may further include atransformation layer. For each of the at least one training sample, thetransformation layer may be configured to warp the first image of thetraining sample according to the corresponding first motion field togenerate the corresponding predicted second image.

In some embodiments, the preliminary model may further include adiscriminator. For each of the at least one training sample, thediscriminator may be configured to generate a discrimination resultbetween the second image of the training sample and the correspondingpredicted second image. The value of the loss function may be determinedfurther based on the discrimination result of each training sample.

In some embodiments, the preliminary model may further include a secondgenerator. For each training sample, the second generator may beconfigured to predict a second motion field from the second image of thetraining sample to the first image of the training sample based on thefirst image and the second image of the training sample.

In some embodiments, the training the preliminary model may includetraining the generator. The designating at least one a portion of thetrained preliminary model as the motion prediction model may includedesignating the trained generator as the motion prediction model.

In some embodiments, the at least one processor may be configured todirect the system to obtain a first annotated image of the sample ROIcorresponding a third motion phase and an unannotated image of thesample ROI corresponding a fourth motion phase. The first annotatedimage may include an annotation of a first feature point relating to theROI. The at least one processor may also be configured to direct thesystem to determine a motion field of the first feature point from thethird motion phase to the fourth motion phase by applying the motionprediction model to the first annotated image and the unannotated image.The at least one processor may further be configured to direct thesystem to generate a second annotated image of the sample ROIcorresponding the fourth motion phase based on the annotation of thefirst feature point and the motion field. The second annotated image mayinclude an annotation of a second feature point corresponding to thefirst feature point.

According to another aspect of the present disclosure, a non-transitorycomputer-readable storage medium including a set of instructions forphysiological motion measurement is provided. When executed by at leastone processor, the set of instructions direct the at least one processorto effectuate a method. The method may include acquiring a referenceimage of an ROI corresponding to a reference motion phase of the ROI anda target image of the ROI corresponding to a target motion phase of theROI, wherein the target motion phase may be different from the referencemotion phase. The method may also include determining a motion field ofthe one or more feature points from the reference motion phase to thetarget motion phase using a motion prediction model, wherein an input ofthe motion prediction model includes at least the reference image andthe target image. The method may further include determining aphysiological condition of the ROI based on the motion field.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determininga physiological condition of an ROI according to some embodiments of thepresent disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generating amotion prediction model using an unsupervised learning techniqueaccording to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for minimizing aloss function to generate a motion prediction model according to someembodiments of the present disclosure;

FIG. 8 is a schematic diagram illustrating the cardiac motion of a heartaccording to some embodiments of the present disclosure;

FIG. 9A illustrates schematic diagrams illustrating exemplary short-axisCMRI images according to some embodiments of the present disclosure;

FIG. 9B illustrates schematic diagrams illustrating exemplary long-axisCMRI images according to some embodiments of the present disclosure;

FIG. 10 illustrates a schematic diagram illustrating an exemplaryapplication of a motion prediction model according to some embodimentsof the present disclosure;

FIG. 11 is a schematic diagram illustrating an exemplary preliminarymodel according to some embodiments of the present disclosure; and

FIG. 12 is a schematic diagram illustrating another exemplarypreliminary model according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2 ) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. The term “image” in the present disclosure isused to collectively refer to image data (e.g., scan data, projectiondata) and/or images of various forms, including a two-dimensional (2D)image, a three-dimensional (3D) image, a four-dimensional (4D), etc. Theterm “pixel” and “voxel” in the present disclosure are usedinterchangeably to refer to an element of an image.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and methods for non-invasive biomedicalimaging, such as for disease diagnostic or research purposes. In someembodiments, the systems may include a single modality imaging systemand/or a multi-modality imaging system. The single modality imagingsystem may include, for example, an ultrasound imaging system, an X-rayimaging system, an computed tomography (CT) system, a magnetic resonanceimaging (MRI) system, an ultrasonography system, a positron emissiontomography (PET) system, an optical coherence tomography (OCT) imagingsystem, an ultrasound (US) imaging system, an intravascular ultrasound(IVUS) imaging system, a near infrared spectroscopy (NIRS) imagingsystem, a far infrared (FIR) imaging system, or the like, or anycombination thereof. The multi-modality imaging system may include, forexample, an X-ray imaging-magnetic resonance imaging (X-ray-MRI) system,a positron emission tomography-X-ray imaging (PET-X-ray) system, asingle photon emission computed tomography-magnetic resonance imaging(SPECT-MRI) system, a positron emission tomography-computed tomography(PET-CT) system, a C-arm system, a digital subtractionangiography-magnetic resonance imaging (DSA-MRI) system, etc. It shouldbe noted that the imaging system described below is merely provided forillustration purposes, and not intended to limit the scope of thepresent disclosure.

The term “imaging modality” or “modality” as used herein broadly refersto an imaging method or technology that gathers, generates, processes,and/or analyzes imaging information of a subject. The subject mayinclude a biological subject and/or a non-biological subject. Thebiological subject may be a human being, an animal, a plant, or aportion thereof (e.g., a cell, a tissue, an organ, etc.). In someembodiments, the subject may be a man-made composition of organic and/orinorganic matters that are with or without life.

An aspect of the present disclosure relates to systems and methods forphysiological motion measurement. The systems and methods may acquire areference image corresponding to a reference motion phase of an ROI(e.g., the heart or a lung of a patient) and a target imagecorresponding to a target motion phase of the ROI, wherein the targetmotion phase being different from the reference motion phase. Thesystems and methods may identify one or more feature points relating tothe ROI from the reference image. The systems and methods may alsodetermine a motion field of the feature point(s) from the referencemotion phase to the target motion phase using a motion prediction model,wherein an input of the motion prediction model may include at least thereference image and the target image. The systems and methods mayfurther determine a physiological condition of the ROI based on themotion field.

According to some embodiments of the present disclosure, the motionfield of the feature point(s) from the reference motion phase to thetarget motion phase may be determined using a motion prediction model.The motion prediction model may be a neural network model that isconfigured to receive two images corresponding to different motionphases of the ROI as an input and output a motion field between the twoimages. In some embodiments of the present disclosure, the physiologicalmotion measurement of the ROI does not rely on a prior defined shapemodel of the ROI. Instead, a motion prediction model, which learns anoptimal mechanism for determining a motion field between two images fromtraining data, may be used for physiological motion measurement. Thismay improve the accuracy and reliability of the measurement result. Inaddition, in some embodiments, the motion prediction model may betrained using one or more training samples according to an unsupervisedlearning technique (or referred to as an unsupervised training processor technique). Also, this may obviate the need for annotating thetraining sample(s), which may improve the efficiency of training themotion prediction model and/or automate the training process.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. As shown,the imaging system 100 may include an imaging device 110, a network 120,one or more terminals 130, a processing device 140, and a storage device150. In some embodiments, the imaging device 110, the terminal(s) 130,the processing device 140, and/or the storage device 150 may beconnected to and/or communicate with each other via a wirelessconnection (e.g., the network 120), a wired connection, or a combinationthereof. The connection between the components of the imaging system 100may be variable. Merely by way of example, the imaging device 110 may beconnected to the processing device 140 through the network 120, asillustrated in FIG. 1 . As another example, the imaging device 110 maybe connected to the processing device 140 directly or through thenetwork 120. As a further example, the storage device 150 may beconnected to the processing device 140 through the network 120 ordirectly.

The imaging device 110 may generate or provide image data related to asubject via scanning the subject. In some embodiments, the subject mayinclude a biological subject and/or a non-biological subject. Forexample, the subject may include a specific portion of a body, such as ahead, a thorax, an abdomen, or the like, or a combination thereof. Insome embodiments, the imaging device 110 may include a single-modalityscanner (e.g., an MRI device, a CT scanner) and/or multi-modalityscanner (e.g., a PET-CT scanner) as described elsewhere in thisdisclosure. In some embodiments, the image data relating to the subjectmay include projection data, one or more images of the subject, etc. Theprojection data may include raw data generated by the imaging device 110by scanning the subject and/or data generated by a forward projection onan image of the subject.

In some embodiments, the imaging device 110 may include a gantry 111, adetector 112, a detecting region 113, a scanning table 114, and aradioactive scanning source 115. The gantry 111 may support the detector112 and the radioactive scanning source 115. The object may be placed onthe scanning table 114 to be scanned. The radioactive scanning source115 may emit radioactive rays to the object. The radiation may include aparticle ray, a photon ray, or the like, or a combination thereof. Insome embodiments, the radiation may include a plurality of radiationparticles (e.g., neutrons, protons, electron, p-mesons, heavy ions), aplurality of radiation photons (e.g., X-ray, a g-ray, ultraviolet,laser), or the like, or a combination thereof. The detector 112 maydetect radiations and/or radiation events (e.g., gamma photons) emittedfrom the detecting region 113. In some embodiments, the detector 112 mayinclude a plurality of detector units. The detector units may include ascintillation detector (e.g., a cesium iodide detector) or a gasdetector. The detector unit may be a single-row detector or a multi-rowsdetector.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., theimaging device 110, the processing device 140, the storage device 150,the terminal(s) 130) may communicate information and/or data with one ormore other components of the imaging system 100 via the network 120. Forexample, the processing device 140 may obtain image data from theimaging device 110 via the network 120. As another example, theprocessing device 140 may obtain user instruction(s) from theterminal(s) 130 via the network 120.

The network 120 may be or include a public network (e.g., the Internet),a private network (e.g., a local area network (LAN)), a wired network, awireless network (e.g., an 802.11 network, a Wi-Fi network), a framerelay network, a virtual private network (VPN), a satellite network, atelephone network, routers, hubs, switches, server computers, and/or anycombination thereof. For example, the network 120 may include a cablenetwork, a wireline network, a fiber-optic network, a telecommunicationsnetwork, an intranet, a wireless local area network (WLAN), ametropolitan area network (MAN), a public telephone switched network(PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 120 may include one or more network accesspoints. For example, the network 120 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the imaging system 100may be connected to the network 120 to exchange data and/or information.

The terminal(s) 130 may be connected to and/or communicate with theimaging device 110, the processing device 140, and/or the storage device150. For example, the terminal(s) 130 may display a physiologicalcondition of an ROI of the subject. In some embodiments, the terminal(s)130 may include a mobile device 131, a tablet computer 132, a laptopcomputer 133, or the like, or any combination thereof. For example, themobile device 131 may include a mobile phone, a personal digitalassistant (PDA), a gaming device, a navigation device, a point of sale(POS) device, a laptop, a tablet computer, a desktop, or the like, orany combination thereof. In some embodiments, the terminal(s) 130 mayinclude an input device, an output device, etc. In some embodiments, theterminal(s) 130 may be part of the processing device 140.

The processing device 140 may process data and/or information obtainedfrom the imaging device 110, the storage device 150, the terminal(s)130, or other components of the imaging system 100. In some embodiments,the processing device 140 may be a single server or a server group. Theserver group may be centralized or distributed. For example, theprocessing device 140 may generate a motion prediction model by traininga preliminary model using one or more training samples. As anotherexample, the processing device 140 may apply the motion prediction modelin physiological motion measurement. In some embodiments, the motionprediction model may be generated by a processing device, while theapplication of the motion prediction model may be performed on adifferent processing device. In some embodiments, the motion predictionmodel may be generated by a processing device of a system different thanthe imaging system 100 or a server different than the processing device140 on which the application of the motion prediction model isperformed. For instance, the motion prediction model may be generated bya first system of a vendor who provides and/or maintains such a motionprediction model, while the physiological motion measurement based onthe provided motion prediction model may be performed on a second systemof a client of the vendor. In some embodiments, the application of themotion prediction model may be performed online in response to a requestfor physiological motion measurement. In some embodiments, the motionprediction model may be determined or generated offline.

In some embodiments, the processing device 140 may be local to or remotefrom the imaging system 100. For example, the processing device 140 mayaccess information and/or data from the imaging device 110, the storagedevice 150, and/or the terminal(s) 130 via the network 120. As anotherexample, the processing device 140 may be directly connected to theimaging device 110, the terminal(s) 130, and/or the storage device 150to access information and/or data. In some embodiments, the processingdevice 140 may be implemented on a cloud platform. For example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or a combination thereof. In some embodiments,the processing device 140 may be implemented by a computing device 200having one or more components as described in connection with FIG. 2 .

In some embodiments, the processing device 140 may include one or moreprocessors (e.g., single-core processor(s) or multi-core processor(s)).Merely by way of example, the processing device 140 may include acentral processing unit (CPU), an application-specific integratedcircuit (ASIC), an application-specific instruction-set processor(ASIP), a graphics processing unit (GPU), a physics processing unit(PPU), a digital signal processor (DSP), a field-programmable gate array(FPGA), a programmable logic device (PLD), a controller, amicrocontroller unit, a reduced instruction-set computer (RISC), amicroprocessor, or the like, or any combination thereof.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the processing device 140, the terminal(s) 130, and/or theimaging device 110. In some embodiments, the storage device 150 maystore data and/or instructions that the processing device 140 mayexecute or use to perform exemplary methods described in the presentdisclosure. In some embodiments, the storage device 150 may include amass storage device, a removable storage device, a volatileread-and-write memory, a read-only memory (ROM), or the like, or anycombination thereof. Exemplary mass storage devices may include amagnetic disk, an optical disk, a solid-state drive, etc. Exemplaryremovable storage devices may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 150 may be implemented on acloud platform as described elsewhere in the disclosure.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components of theimaging system 100 (e.g., the processing device 140, the terminal(s)130). One or more components of the imaging system 100 may access thedata or instructions stored in the storage device 150 via the network120. In some embodiments, the storage device 150 may be part of theprocessing device 140.

It should be noted that the above description of the imaging system 100is intended to be illustrative, and not to limit the scope of thepresent disclosure. Many alternatives, modifications, and variationswill be apparent to those skilled in the art. The features, structures,methods, and other characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. For example, the imagingsystem 100 may include one or more additional components. Additionallyor alternatively, one or more components of the imaging system 100described above may be omitted. As another example, two or morecomponents of the imaging system 100 may be integrated into a singlecomponent.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device 200 according to someembodiments of the present disclosure. The computing device 200 may beused to implement any component of the imaging system 100 as describedherein. For example, the processing device 140 and/or the terminal 130may be implemented on the computing device 200, respectively, via itshardware, software program, firmware, or a combination thereof. Althoughonly one such computing device is shown, for convenience, the computerfunctions relating to the imaging system 100 as described herein may beimplemented in a distributed fashion on a number of similar platforms,to distribute the processing load. As illustrated in FIG. 2 , thecomputing device 200 may include a processor 210, a storage 220, aninput/output (I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the imaging device 110, the terminal(s) 130, the storagedevice 150, and/or any other component of the imaging system 100. Insome embodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method operations that are performedby one processor as described in the present disclosure may also bejointly or separately performed by the multiple processors. For example,if in the present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the imagingdevice 110, the terminal(s) 130, the storage device 150, and/or anyother component of the imaging system 100. In some embodiments, thestorage 220 may include a mass storage device, a removable storagedevice, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or any combination thereof. In some embodiments, the storage220 may store one or more programs and/or instructions to performexemplary methods described in the present disclosure. For example, thestorage 220 may store a program for the processing device 140 to executeto generate a motion prediction model.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. The input device may includealphanumeric and other keys that may be input via a keyboard, a touchscreen (for example, with haptics or tactile feedback), a speech input,an eye tracking input, a brain monitoring system, or any othercomparable input mechanism. The input information received through theinput device may be transmitted to another component (e.g., theprocessing device 140) via, for example, a bus, for further processing.Other types of the input device may include a cursor control device,such as a mouse, a trackball, or cursor direction keys, etc. The outputdevice may include a display (e.g., a liquid crystal display (LCD), alight-emitting diode (LED)-based display, a flat panel display, a curvedscreen, a television device, a cathode ray tube (CRT), a touch screen),a speaker, a printer, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and theimaging device 110, the terminal(s) 130, and/or the storage device 150.The connection may be a wired connection, a wireless connection, anyother communication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee™ link, a mobilenetwork link (e.g., 3G, 4G, 5G), or the like, or a combination thereof.In some embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device 300 according to some embodimentsof the present disclosure. In some embodiments, one or more components(e.g., a terminal 130 and/or the processing device 140) of the imagingsystem 100 may be implemented on the mobile device 300.

As illustrated in FIG. 3 , the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS™, Android™,Windows Phone™) and one or more applications 380 may be loaded into thememory 360 from the storage 390 in order to be executed by the CPU 340.The applications 380 may include a browser or any other suitable mobileapps for receiving and rendering information relating to imageprocessing or other information from the processing device 140. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing device 140 and/or other components of theimaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices 140A and 140B according to some embodiments of the presentdisclosure. The processing devices 140A and 140B may be exemplaryprocessing devices 140 as described in connection with FIG. 1 . In someembodiments, the processing device 140A may be configured to apply amotion prediction model in physiological condition measurement. Theprocessing device 140B may be configured to obtain one or more trainingsamples and/or generate the motion prediction model using the trainingsamples. In some embodiments, the processing devices 140A and 140B maybe respectively implemented on a processing unit (e.g., a processor 210illustrated in FIG. 2 or a CPU 340 as illustrated in FIG. 3 ). Merely byway of example, the processing devices 140A may be implemented on a CPU340 of a terminal device, and the processing device 140B may beimplemented on a computing device 200. Alternatively, the processingdevices 140A and 140B may be implemented on a same computing device 200or a same CPU 340. For example, the processing devices 140A and 140B maybe implemented on a same computing device 200.

As shown in FIG. 4A, the processing device 140A may include anacquisition module 401, an identification module 402, a motion fielddetermination module 403, and a physiological condition determinationmodule 404.

The acquisition module 401 may be configured to acquire a plurality ofimages indicative of a physiological motion of an ROI. The imagesindicative of the physiological motion of the ROI may correspond to aplurality of motion phases of the ROI. For example, the images maycorrespond to a plurality of cardiac phases and indicative of a cardiacmotion of the heart. In some embodiments, the images may include atleast a reference image of the ROI corresponding to a reference motionphase and a target image of the ROI corresponding to a target motionphase. The images may be obtained by an image acquisition device viascanning a patient or retrieved from a storage device. More descriptionsregarding the acquisition of the images indicative of the physiologicalmotion of the ROI may be found elsewhere in the present disclosure. See,e.g., operation 501 in FIG. 5 and relevant descriptions thereof.

The identification module 402 may be configured to identify one or morefeature points relating to the ROI from the reference image. A featurepoint may refer to a representative point of the ROI which can be usedto measure the physiological motion of the ROI. In some embodiments, thefeature point(s) may be identified from the reference image according toa user input, or by the processing device 140A automatically orsemi-automatically. More descriptions regarding the identification ofthe feature point(s) may be found elsewhere in the present disclosure.See, e.g., operation 502 in FIG. 5 and relevant descriptions thereof.

The motion field determination module 403 may be configured to determinea motion field of the feature point(s) from the reference motion phaseto the target motion phase using a motion prediction model. The motionfield of the feature point(s) may include one or more motion vectors,each of which corresponds to one of the feature points. The motionprediction model may receive a pair of images corresponding to twodifferent motion phases of the ROI, and output a motion field between(or with respect to) the pair of images. More descriptions regarding thedetermination of the motion field of the feature points may be foundelsewhere in the present disclosure. See, e.g., operation 503 in FIG. 5and relevant descriptions thereof.

The physiological condition determination module 404 may be configuredto determine a physiological condition of the ROI based on the motionfield. The physiological condition of the ROI may indicate a healthstatus of the ROI. In some embodiments, based on the motion field, thephysiological condition determination module 404 may determine a valueof a biological parameter indicating the physiological condition of theROI, an analyzing result of the physiological condition of the ROI, orthe like, or any combination thereof. More descriptions regarding thedetermination of the physiological condition of the ROI may be foundelsewhere in the present disclosure. See, e.g., operation 504 in FIG. 5and relevant descriptions thereof.

As shown in FIG. 4B, the processing device 140B may include anacquisition module 405 and a model generation module 406.

The acquisition module 405 may be configured to obtain one or moretraining samples. Each training sample may include a first image and asecond image indicative of a physiological motion of a sample ROI,wherein the first and second images may correspond to a first motionphase and a second motion field of the sample ROI, respectively. Moredescriptions regarding the acquisition of the training samples may befound elsewhere in the present disclosure. See, e.g., operation 601 inFIG. 6 and relevant descriptions thereof.

The model generation module 406 may be configured to generate the motionprediction model by training a preliminary model using the trainingsamples according to an unsupervised learning technique. In someembodiments, the model generation module 406 may train the preliminarymodel by minimizing a loss function. At least a portion of the trainedpreliminary model may be designated as the motion prediction model. Moredescriptions regarding the generation of the motion prediction model maybe found elsewhere in the present disclosure. See, e.g., operation 602in FIG. 6 and relevant descriptions thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, the processing device 140A and/or the processing device140B may share two or more of the modules, and any one of the modulesmay be divided into two or more units. For instance, the processingdevices 140A and 140B may share a same acquisition module; that is, theacquisition module 401 and the acquisition module 405 are a same module.In some embodiments, the processing device 140A and/or the processingdevice 140B may include one or more additional modules, such a storagemodule (not shown) for storing data. In some embodiments, the processingdevice 140A and the processing device 140B may be integrated into oneprocessing device 140.

FIG. 5 is a flowchart illustrating an exemplary process for determininga physiological condition of an ROI according to some embodiments of thepresent disclosure. In some embodiments, process 500 may be executed bythe imaging system 100. For example, the process 500 may be implementedas a set of instructions (e.g., an application) stored in a storagedevice (e.g., the storage device 150, the storage 220, and/or thestorage 390). In some embodiments, the processing device 140A (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4A) mayexecute the set of instructions and may accordingly be directed toperform the process 500.

As used herein, an ROI may include any region of a subject (e.g., apatient or another organism) that undergoes a physiological motion. Forillustration purposes, the following descriptions take a patient as anexemplary subject, and not intended to limit the scope of the presentdisclosure. Exemplary ROIs of a patient may be the heart that undergoesa cardiac motion, a lung that undergoes a respiratory motion, a regionfilled with blood which forms a blood flow, the stomach that undergoes agastrointestinal motion, the brain that undergoes a brain motion (e.g.,has a brain blood flow), a chest that has a physiological motion causedby the cardiac motion, an abdomen that has a physiological motion causedby the respiratory motion, or the like, or any combination thereof. Insome embodiments, the ROI may include the heart of the patient. Asillustrated in FIG. 8 , the heart may undergo contraction and relaxationmotions in a radial direction, a circumferential direction, and alongitudinal direction.

In 501, the processing device 140A (e.g., the acquisition module 401,the interface circuits of the processor 210) may acquire a plurality ofimages indicative of the physiological motion of the ROI.

The images indicative of the physiological motion of the ROI maycorrespond to a plurality of motion phases of the ROI. For example, acardiac cycle may include systole (during which the left and rightventricles contract and eject blood into the aorta and pulmonary artery,respectively) and diastole (during which the ventricles are relaxed).The cardiac cycle may be divided into a plurality of cardiac phases,such as 5 or 10 cardiac phases depending on, for example, the heart rateand/or movement amplitude of the heart. The images may correspond to theplurality of cardiac phases and indicative of the cardiac motion of theheart. As another example, a respiratory cycle may include aninspiratory phase (during which the chest of the subject expands and airflows into the lungs) and an expiratory phase (during which the chestshrinks and the air is pushed out of the lungs). The respiratory cyclemay be gated into a plurality of respiratory phases, such as 4respiratory phases including a mid-inspiratory phase, an end-inspiratoryphase, a mid-expiratory phase, and an end-expiratory phase according to,for example, time or the amplitude of the respiratory motion. The imagesmay correspond to the plurality of respiratory phases and indicative ofthe respiratory motion of the lung.

In some embodiments, the images may include at least a reference imageof the ROI corresponding to a reference motion phase and a target imageof the ROI corresponding to a target motion phase. The reference motionphase and the target motion phase may be any two different motion phasesof the ROI. Merely by way of example, for the cardiac motion, thereference motion phase may be an end of diastole (ED) phase and thetarget motion phase may an end of systole (ES) phase, or the referencemotion phase may be an ES phase and the target motion phase may be an EDphase. As another example, for the respiratory motion, the referencemotion phase may be an end-inspiratory phase and the target motion phasemay an end-expiratory phase, or the reference motion phase may be anend-expiratory phase and the target motion phase may be anend-inspiratory phase.

In some embodiments, the images may include a 2D image (e.g., a sliceimage), a 3D image, a 4D image (e.g., a series of 3D images with respectto time), and/or any related image data (e.g., scan data, projectiondata, etc.). In some embodiments, the images may be reconstructed basedon image data acquired using an image acquisition device, such as theimaging device 110 of the system 100 or an external image acquisitiondevice. For example, the images may be acquired by a CT device, an MRIdevice, an ultrasonography system, an X-ray device, a PET device, or thelike, by performing a scan of the patient. In some embodiments, theimages may include a plurality of cardiac MRI (CMRI) images acquired byan MRI device by executing an MR scan on the patient. During the MRscan, an electrocardiogram (ECG) signal representing the cardiac motionof the patient may be acquired. A cardiac cycle of the patient may bedivided into a plurality of cardiac phases according to the ECG signal,and the image data acquired in the MR scan may be divided into aplurality of image data sets corresponding to the cardiac phases. Then,the CMRI images may be reconstructed based on the image data sets. Insome embodiments, the CMRI images may include short-axis image(s) (e.g.,images 901 and 902 in FIG. 9A) and/or long-axis image(s) (e.g., images907 and 908 in FIG. 9B) of the heart. A long-axis image may illustrate across-section of the heart along the longitudinal direction asillustrated in FIG. 8 . A short-axis image may illustrate across-section of the heart that is perpendicular to the longitudinaldirection. In some embodiments, the images may be previously generatedand stored in a storage device (e.g., the storage device 150, thestorage 220, the storage 390, or an external source). The processingdevice 140A may retrieve the images from the storage device.

In 502, the processing device 140A (e.g., the identification module 402,the processing circuits of the processor 210) may identify one or morefeature points relating to the ROI from the reference image.

As used herein, a feature point may refer to a representative point ofthe ROI which can be used to measure the physiological motion of theROI. For example, if the ROI is a lung of a patient, the featurepoint(s) relating to the lung in the reference image may include one ormore bifurcations of a blood vessel or a trachea in the lung. As anotherexample, if the ROI is the heart of a patient, the feature point(s)relating to the heart in the reference image may include one or morepairs of feature points. Each pair of feature points may include aninner point on an endocardium (i.e., an inner border of the myocardium)of the heart and a corresponding outer point on an epicardium (i.e., anouter border of the myocardium) of the heart. As used herein, an innerpoint and an outer point that are located along (or nearly locatedalong) a same straight line passing through a center of the heart (e.g.,a center of gravity of the left ventricle) may be regarded as beingcorresponding to each other. For example, a center of the heart in ashort axis CMRI image may refer to a center of the endocardium or theepicardium of the heart in the short axis CMRI image.

In some embodiments, the feature point(s) may be identified from thereference image according to a user input. For example, via a userinterface implemented on, e.g., a terminal 130 or a mobile device 300, auser may manually mark one or more feature points in the referenceimage. Alternatively, the feature point(s) may be identified from thereference image automatically by the processing device 140A.Alternatively, the feature point(s) may be identified from the referenceimage by the processing device 140A semi-automatically. For example, thefeature point identification may be performed by the processing device140A based on an image analysis algorithm in combination with userintervention. Exemplary user interventions may include providing aparameter relating to the image analysis algorithm, providing a positionparameter relating to a feature point, making an adjustment to orconfirming a preliminary feature point identified by the processingdevice 140A, providing instructions to cause the processing device 140Ato repeat or redo the feature point identification, etc.

For illustration purposes, an exemplary process for identifying an innerpoint and a corresponding outer point of the heart from a reference mageis provided hereinafter. In some embodiments, the processing device 140Amay segment an endocardium and an epicardium of the heart from thereference image. For example, a user may manually annotate theendocardium and the epicardium from the reference image via a userinterface, and the processing device 140A may segment the endocardiumand the epicardium according to the user annotation. As another example,the endocardium and the epicardium of the myocardium may be segmented bythe processing device 140A automatically according to an image analysisalgorithm (e.g., an image segmentation algorithm). Alternatively, theendocardium and the epicardium may be segmented by the processing device140A semi-automatically based on an image analysis algorithm incombination with information provided by a user. Exemplary informationprovided by the user may include a parameter relating to the imageanalysis algorithm, a position parameter relating to the endocardium andthe epicardium, an adjustment to or confirmation of a preliminaryendocardium and/or a preliminary epicardium generated by the processingdevice 140A, etc.

After the endocardium and the epicardium of the myocardium aresegmented, the processing device 140A may identify the inner point atthe endocardium and the corresponding outer point at the epicardium fromthe reference image based on the positions of the endocardium and theepicardium. Similar to the determination of the endocardium and theepicardium as described above, the inner point and the correspondingouter point may be determined according to a use annotation regardingthe inner point and the corresponding outer point. Alternatively, theinner point and the corresponding outer point may be determinedautomatically based on the positions of the endocardium and theepicardium by the processing device 140A. For example, the processingdevice 140A may determine an intersection point between the epicardiumand a line connecting the inner point and the heart center, anddesignate the intersection point as the corresponding outer point of theinner point. As another example, the processing device 140A may utilizea Laplace equation between the endocardium and epicardium to determinethe inner point and the corresponding outer point. In some embodiments,the processing device 140A may determine a circle (denoted as C1) thathas a central point coincident with the heart center and passes throughthe inner point. Using the Laplace equation, the processing device 140Amay further determine one or more candidate circles by expending theradius of the circle C1. For each candidate circle, the processingdevice 140A may determine an intersection point between the candidatecircle and a normal of the circle C1 at the inner point. Theintersection point of a certain candidate circle that is located at theepicardium may be determined as the outer point corresponding to theinner point.

In some embodiments, the processing device 140A may identify a pluralityof pairs of inner point and outer point from the reference image of theheart. For example, the processing device 140A may segment the heartinto a plurality of sub-regions, each of which may include a specificartery. For each sub-region, an inner point and a corresponding outerpoint may be identified from the sub-region, wherein the identifiedinner point and the outer point may be used in analyzing thephysiological condition of the sub-region.

In 503, the processing device 140A (e.g., the motion field determinationmodule 403, the processing circuits of the processor 210) may determinea motion field of the feature point(s) from the reference motion phaseto the target motion phase using a motion prediction model.

The motion field of the feature point(s) may include one or more motionvectors, each of which corresponds to one of the feature point(s). Amotion vector of a feature point may be used to describe a motion of thefeature point between the reference motion phase and the target motionphase. Merely by way of example, a location of a certain feature pointin the reference image may be represented as a first coordinate (X1, Y1,Z1). The certain feature point may have a corresponding point in thetarget image that represents a same physical point of the ROI as thecertain feature point. The location of the point corresponding to thecertain feature point may be represented as a second coordinate (X2, Y2,Z2). The motion vector of the certain feature point from the referencemotion phase to the target motion phase may be (Ux, Uy, Uz), wherein Ux,Uy, and Uz may be equal to (X1-X2), (Y1-Y2), and (Z1-Z2), respectively.In some embodiments, there may be a plurality of feature points relatingto the ROI. The motion field may include motion vector(s) of all or aportion of the plurality of feature points between the reference andtarget motion phases.

As used herein, a motion prediction model may refer to a neural networkmodel configured to receive a pair of images corresponding to twodifferent motion phases of the ROI, and output a motion field between(or with respect to) the pair of images. For example, as shown in FIG.10 , a reference image 1001 and a target image 1002 corresponding to twocardiac phases may be inputted into the motion prediction model, and themotion prediction model may output a motion field from the referenceimage 1001 to the target image 1002. The motion field outputted by themotion prediction model may include a motion vector of each point in thereference image 1001 from the reference motion phase to the targetmotion phase. A motion vector of a feature point 1003A in the referenceimage 1001 may be determined based on the motion field of the entirereference image 1001 by the processing device 140A. Optionally, afeature point 1003B corresponding to the feature point 1003A may beidentified in the target image 1002 based on the motion vector of thefeature point 1003A.

In some embodiments, the motion prediction model may be obtained fromone or more components of the imaging system 100 or an external sourcevia a network (e.g., the network 120). For example, the motionprediction model may be previously trained by a computing device (e.g.,the processing device 140B), and stored in a storage device (e.g., thestorage device 150, the storage 220, and/or the storage 390) of theimaging system 100. The processing device 140A may access the storagedevice and retrieve the motion prediction model. In some embodiments,the motion prediction model may be generated according to a machinelearning algorithm. The machine learning algorithm may include but notbe limited to an artificial neural network algorithm, a deep learningalgorithm, a decision tree algorithm, an association rule algorithm, aninductive logic programming algorithm, a support vector machinealgorithm, a clustering algorithm, a Bayesian network algorithm, areinforcement learning algorithm, a representation learning algorithm, asimilarity and metric learning algorithm, a sparse dictionary learningalgorithm, a genetic algorithm, a rule-based machine learning algorithm,or the like, or any combination thereof.

In some embodiments, the motion prediction model may be trained by acomputing device (e.g., the processing device 140B or an externalprocessing device) using a supervised learning algorithm (or referred toas a supervised training process or technique). Merely by way ofexample, the computing device may obtain one or more annotated trainingsamples. Each of the annotated training sample(s) may include a firstannotated image of a sample ROI corresponding to a first motion phase, asecond annotated image of the sample ROI corresponding to a secondmotion phase, and a sample motion field between (or with respect to) thefirst annotated image and the second annotated image. For an annotatedtraining sample, the first annotated image may be annotated with one ormore first sample feature points relating to the sample ROI, and thesecond annotated image may be annotated with one or more second samplefeature points corresponding to the first sample feature points. Thesample motion field of the annotated training sample may be determinedbased on the first annotated image and the second annotated imageaccording to an image registration technique. In some embodiments, thesample ROI may be of a same type as the ROI. As used herein, two ROIsare deemed to be of a same type when they belong to a same type of organor tissue. The first and second annotated images of each annotatedtraining sample may be of a same type of image as the reference imageand the target image as described above. As used herein, two images aredeemed to be of a same type when they are generated using a same type ofimaging technique (e.g., an MRI technique, a CT technique). The firstand second sample feature points of each annotated training sample maybe annotated manually, automatically, or semi-automatically according toa feature point identification technique as described elsewhere in thisdisclosure (e.g., 502 and the relevant descriptions).

The computing device may further generate the motion prediction model bytraining a first preliminary model using the annotated trainingsample(s) according to a supervised learning technique. Merely by way ofexample, the first and second annotated images of each annotatedtraining sample may be inputted into the first preliminary model, whichmay output a predicted motion field from the first annotated image tothe second annotated image. The computing device may determine a valueof a first loss function based on the predicted motion field and theknown sample motion field of each annotated training sample. Forexample, the first loss function may measure the difference(s) betweenthe predicted motion field and the sample motion field of the annotatedtraining sample(s). Alternatively, for each training sample, thecomputing device may determine a predicted motion field and an actualmotion field of the first sample feature point(s) from the first motionphase to the second motion phase based on the predicted motion field andthe sample motion field of the entire first annotated image,respectively. The first loss function may measure a difference betweenthe predicted and actual motion fields of the first sample featurepoint(s) of each annotated training sample. The first preliminary modelmay be iteratively trained to minimize the first loss function. Thetrained model of the first preliminary model may be designated as themotion prediction model.

In some embodiments, the motion prediction model may be trained by acomputing device (e.g., the processing device 140B or an externalprocessing device) using an unsupervised learning algorithm. Forexample, the motion prediction model may be trained using one or moreunannotated training samples. More descriptions regarding the generationof the motion prediction model according to an unsupervised learningtechnique may be found elsewhere in the present disclosure. See, e.g.,FIG. 6 and relevant descriptions thereof.

In 504, the processing device 140A (e.g., the physiological conditiondetermination module 404, the processing circuits of the processor 210)may determine a physiological condition of the ROI based on the motionfield.

The physiological condition of the ROI may indicate a health status ofthe ROI. For example, based on the motion field, the processing device140A may determine a value of a biological parameter indicating thephysiological condition of the ROI, an analyzing result of thephysiological condition of the ROI (e.g., a determination as to whetherthe value of a biological parameter of the ROI is within a normalregion, a predicted risk that the ROI has a certain disease, a treatmentsuggestion regarding the ROI), or the like, or any combination thereof.

For illustration purposes, the following descriptions are described withreference to the determination of a physiological condition of the heartof a patient, and not intended to limit the scope of the presentdisclosure. In some embodiments, the processing device 140A maydetermine the physiological condition of the heart directly based on themotion field. Merely by way of example, the feature point(s) determinedin 502 may include a plurality feature points relating to the heart. Theprocessing device 140A may determine a motion parameter of the wholeheart to indicate the physiological condition of the whole heart. Themotion parameter may be, for example, an average value, a maximum value,a minimum value, or the like, of all or a portion of the motion vectorsof the feature points. The processing device 140A may determine if themotion parameter is within a normal range. A motion parameter out of thenormal range may indicate that the heart is in an abnormal state (e.g.,having a myocardial dysfunction). As another example, the feature pointsmay be located at different sub-regions of the heart. The processingdevice 140A may determine a physiological condition of a certainsub-region by, for example, analyzing a motion parameter of the certainsub-region based on the motion vector(s) of the feature point(s) in thecertain sub-region. Alternatively, the processing device 140A maydetermine a physiological condition of the heart by comparing the motionparameters of different sub-regions. For example, if a motion parameterof a certain sub-region is greater than an average motion parameter ofall the sub-regions by a threshold, the certain sub-region may beconsidered as in an abnormal state.

In some embodiments, the processing device 140A may determine abiological parameter of the heart based on the motion field, and analyzethe physiological condition of the heart according to the biologicalparameter. For example, a strain value relating to the heart may bedetermined for strain analysis. Strain, also be referred to asmyocardial contractility, is a metric for quantifying myocardialdysfunction in patients. As described in connection with 502, thefeature point(s) relating to the heart may include one or more pairs offeature points, each of which includes an inner point and acorresponding outer point. In some embodiments, based on the motionvectors of a pair of inner point and outer point located at a certainsub-region of the heart, the processing device 140A may determine astrain value of the certain sub-region.

FIG. 9A is a schematic diagram illustrating an exemplary reference image901 and a target image 902 of a heart according to some embodiments ofthe present disclosure. The reference image 901 and the target image 902may be short-axis CMRI images corresponding to Phase 0 and Phase t,respectively. A pair of feature points located at a first sub-region ofthe heart, including an inner point 903 and a corresponding outer point904, may be identified from the reference image 901. A strain value inthe radial direction (also be referred to as a radical strain value) anda strain value in the circumferential direction (also referred to as acircumferential strain value) of the first sub-region may be determinedbased on the motion vectors of the inner point 903 and the outer point904.

Merely by way of example, the processing device 140A may determine adistance R₀ between the inner point 903 and the outer point 904 in thePhase 0 by analyzing the reference image 901. The processing device 140Amay also determine a distance R_(t) between the inner point 903 and theouter point 904 in the Phase t based on the motion vectors of the innerpoint 903 and the outer point 904. For example, an inner point 905corresponding to the inner point 903 and an outer point 906corresponding to the outer point 904 may be identified in the targetimage 902 based on the motion vectors of the inner point 903 and theouter point 904, respectively. The distance between the inner point 905and the outer point 906 may be determined as the distance R_(t).Further, the processing device 140A may determine a radial strain valueε_(R) and/or a circumferential strain value ε_(C) of the firstsub-region based on R₀ and R_(t) according to Equations (1) and (2),respectively, as below:

$\begin{matrix}{{ɛ_{R} \approx \frac{R_{t} - R_{0}}{R_{0}}},} & (1) \\{ɛ_{C} \approx {0.5 \cdot {\frac{R_{t}^{2} - R_{0}^{2}}{R_{0}^{2}}.}}} & (2)\end{matrix}$

FIG. 9B is a schematic diagram illustrating an exemplary reference image907 and a target image 908 of a heart according to some embodiments ofthe present disclosure. The reference image 907 and the target image 908may be long-axis CMRI images corresponding to Phase 0 and Phase t,respectively. An outer point 909 located at a second sub-region may beidentified from the reference image 907. A strain value in thelongitudinal direction (also be referred to as a longitudinal strainvalue) of the second sub-region may be determined based on the motionvector of the outer point 909. Merely by way of example, the processingdevice 140A may determine a distance L₀ between the outer point 909 anda reference plane on the upper portion of the heart in the Phase 0 basedon the reference image 907. The processing device 140A may alsodetermine an outer point 910 in the target image 908 based on the motionvector of the outer point 909, wherein the outer point 910 maycorrespond to the inner point 909. The processing device 140A may thendetermine a distance L_(t) between the outer point 910 and a referenceplane on the upper portion of the heart in the Phase t. Further, theprocessing device 140A may determine a longitudinal strain value ε_(L)of the second sub-region based on L₀ and L_(t) according to Equation (3)as below:

$\begin{matrix}{ɛ_{L} \approx {\frac{L_{t} - L_{0}}{L_{0}}.}} & (3)\end{matrix}$

After the strain value of the certain sub-region is determined, theprocessing device 140A may determine the physiological condition of thecertain sub-region according to the determined strain value. Merely byway of example, if the strain value is out of a normal strain range, thecertain sub-region may be considered as in an abnormal condition. Insome embodiments, the strain values of a plurality of sub-regions of theheart may be determined. The strain values of the sub-regions may becompared with each other in order to identify an abnormal sub-region(e.g., a sub-region having a strain value greater than an average strainvalue by a threshold). As another example, the processing device 140Amay determine an overall strain value of the whole heart based on thestain values of different sub-regions, and determine the physiologicalcondition of the whole heart based on the overall strain value.

In some embodiments, in 501, at least three images corresponding to atleast three motion phases may be obtained. The at least three images mayform a plurality of pairs of images. For example, 10 CMRI images, fromthe ED phase to the ES phase or from the ES phase to the ED phase, maybe obtained. Every two images corresponding to consecutive cardiacphases may form a pair of images. Alternatively, any two differentimages corresponding to different cardiac phases may form a pair ofimages. For each pair of images, the processing device 140A may performoperations 502 and 503 to determine a corresponding motion field. Theprocessing device 140A may determine the physiological condition of theROI based on the motion fields. Merely by way of example, a change inthe motion fields over time may be determined to evaluate thephysiological condition of the ROI.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, the process 500 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed above. For example, the process 500may include an additional operation to transmit the physiologicalcondition to a terminal device (e.g., a terminal device 130 of a doctor)for display.

FIG. 6 is a flowchart illustrating an exemplary process for generating amotion prediction model using an unsupervised learning techniqueaccording to some embodiments of the present disclosure. In someembodiments, process 600 may be executed by the imaging system 100. Forexample, the process 600 may be implemented as a set of instructions(e.g., an application) stored in a storage device (e.g., the storagedevice 150, the storage 220, and/or the storage 390). In someembodiments, the processing device 140B (e.g., the processor 210 of thecomputing device 200, the CPU 340 of the mobile device 300, and/or oneor more modules illustrated in FIG. 4B) may execute the set ofinstructions and may accordingly be directed to perform the process 600.Alternatively, the process 600 may be performed by a computing device ofa system of a vendor that provides and/or maintains such motionprediction model, wherein the system of the vendor is different from theimaging system 100. For illustration purposes, the followingdescriptions are described with reference to the implementation of theprocess 600 by the processing device 140B, and not intended to limit thescope of the present disclosure.

In 601, the processing device 140B (e.g., the acquisition module 405,the interface circuits of the processor 210) may obtain one or moretraining samples.

Each training sample may include an image A (or also be referred to as afirst image) and an image B (or also referred be to as a second image)indicative of a physiological motion of a sample ROI, wherein the imagesA and B may correspond to a first motion phase and a second motion fieldof the sample ROI, respectively. As used herein, the sample ROI of atraining sample may refer to an ROI of a sample subject (e.g., a samplepatient) that is used in training the motion prediction model.

In some embodiments, the sample ROI of each training sample may be ofthe same type of as the ROI as described in connection with FIG. 5 . Theimages A and B of each training sample may be of the same type as thereference image and the target image as described in connection withFIG. 5 . For example, the motion prediction model may be used in theprocess 500 to determine a physiological motion of the heart of apatient between two cardiac phases based on two PET images of the heartcorresponding to the two cardiac phases. In such cases, for eachtraining sample, the sample ROI may be the heart of a sample patient,and the images A and B may be PET images of the heart of the samplepatient corresponding to different cardiac phases.

In some embodiments, the images A and B of the training sample(s) may beobtained in a similar manner as acquiring the images of the ROI asdescribed in connection with 501. For example, the images A and B of atraining sample may be obtained by an image acquisition device viascanning a sample patient. Alternatively, the images A and B of atraining sample may be retrieved from a storage device (e.g., thestorage device 150 or an external source) that stores the images A andB.

In 602, the processing device 140B (e.g., the model generation module406, the processing circuits of the processor 210) may generate themotion prediction model by training a preliminary model using thetraining sample(s) according to an unsupervised learning technique.

In some embodiments, the processing device 140B may train thepreliminary model by minimizing a loss function. A loss function of amodel may be used to evaluate the accuracy and reliability of the model,for example, the smaller the loss function is, the more reliable of themodel is. When the loss function of the preliminary model is minimized,the processing device 140B may designate at least a portion of thetrained model as the motion prediction model. In some embodiments, theprocessing device 140B may train the preliminary model by performing oneor more operations in process 700 described in FIG. 7 .

For illustration purposes, an exemplary preliminary model 1100 accordingto some embodiments of the present disclosure is illustrated in FIG. 11. As illustrated in FIG. 11 , the preliminary model 1100 may include agenerator 1102 and a transformation layer 1104. One or more trainingsamples, each of which includes a pair of CMRI images A and B (alsoreferred to as an image pair (A, B)) corresponding to different cardiacphases, may be used to train the preliminary model 1100 in order togenerate a first trained model.

Taking a training sample 1101 shown in FIG. 11 as an example, thegenerator 1102 may be configured to predict a first motion field 1103from the image A to the image B. The transformation layer 1104 may beconfigured to warp the image A according to the first motion field 1103to generate an image B′, which may be regarded as a predicted image B(or also be referred to as a predicted second image). Merely by way ofexample, each pixel (or voxel) in the image A may be transformedaccording to the motion vector of the pixel (or voxel), so as togenerate the image B′ of the training sample 1101. In some embodiments,the generator 1102 and the transformation layer 1104 may be any neuralnetwork component that can realize their respective functions. Merely byway of example, the generator 1102 may be a convolutional neural network(CNN). The transformation layer 1104 may be a spatial transformationnetwork.

In some embodiments, the loss function of the preliminary model 1100 mayrelate to a first difference between the image B and the image B′ ofeach training sample. For example, the training sample(s) may include aplurality of training samples. The loss function may be used to measurean overall level (e.g., an average value) of the first differences ofthe training samples. The loss function may be minimized in the modeltraining so that the first differences between the images B and B′ ofthe training samples may be minimized locally or globally. As usedherein, a difference between two images may be measured by any metricsfor measuring a similarity degree or a difference between the twoimages. Merely by way of example, the difference between two images maybe determined based on an image similarity algorithm, including a peaksignal to noise ratio (PSNR) algorithm, a structural similarity (SSIM)algorithm, a perceptual hash algorithm, a cosine similarity algorithm, ahistogram-based algorithm, a Euclidean distance algorithm, or the like,or any combination thereof.

Optionally, for each training sample, the processing device 140B mayfurther determine a second motion field from the image B to the image Aof the training sample using the preliminary model 1100. For example,for the training sample 1101, the image pair (A, B) may be transformedinto an image pair (B, A), which may be inputted into the generator 1102to obtain the second motion field of the training sample 1101. Theprocessing device 140B may further determine an opposite motion field(or referred to as a reversed motion field) of the second motion fieldof the training sample 1101. Theoretically, if the generator 1102 isaccurate enough (e.g., having an accuracy higher than a threshold), thefirst motion field 1103 may be substantially equal to an opposite motionfield of the second motion field. A second difference between theopposite motion field and the first motion field 1103 may be determinedto indicate the accuracy of the generator 1102. In some embodiments, thesecond difference of each training sample may be taken intoconsideration in training the preliminary model 1100. For example, amotion consistency loss may be determined to measure an overall level ofthe second difference(s) of the training sample(s) and incorporated intothe loss function of the preliminary model 1100. This may improve theconsistency and reliability of the first trained model.

In some embodiments, the preliminary model 1100 may further include adiscriminator 1105 as shown in FIG. 11 . Such preliminary model 1100including the discriminator 1105 may also be referred to as a generativeadversarial network (GAN) model. For the training sample 1101, thediscriminator 1105 may be configured to receive the images B and B′ ofthe training sample 1101, and discern which one is a real image togenerate a discrimination result between the images B and B′. Forexample, the discriminator result may include a determination as towhether the image B is a real image, a probability that the image B is areal image, a determination as to whether the image B′ is a real image,a probability that the image B′ is a real image, or the like, or anycombination thereof. In some embodiments, the discriminator 1105 may beany neural network component that can realize its function. Merely byway of example, the discriminator 1105 may be an image classifier, apatch GAN discriminator, etc. In some embodiments, the loss function ofa preliminary model 1100 that includes the discriminator 1105 may bedetermined based on the first difference, the discrimination result, andoptionally the second difference of each training sample. In someembodiments, the loss function of a preliminary model 1100 that includesthe discriminator 1105 may be a GAN loss.

In some embodiments, the preliminary model may be a preliminary model1200 as illustrated in FIG. 12 . The preliminary model 1200 may betrained using the same or similar training sample(s) of the preliminarymodel 1100 to generate a second trained model. The preliminary model1200 may include a forward pipeline (the left portion illustrated inFIG. 12 ) and a backward pipeline (the right portion illustrated in FIG.12 ). Each of the forward and backward pipelines may include the same orsimilar configuration as the preliminary model 1100 as described inconnection with FIG. 11 . For example, as shown in FIG. 12 , the forwardpipeline may include a generator 1102A, a transformation layer 1104A,and a discriminator 1105A. The backward pipeline may include a generator1102B, a transformation layer 1104B, and a discriminator 1105B.

For the training sample 1101, the image pair (A, B) and the image pair(B, A) may be inputted into the forward pipeline and the backwardpipeline, respectively. The forward pipeline may be used to generate animage B′ (i.e., a predicted image B) by warping the image A according tothe image B. For example, the generator 1102A may predict a motion field1103A from the image A to the image B of the training sample 1101. Thetransformation layer 1104A may generate the image B′ by warping theimage A according to the motion field 1103A. The discriminator 1105A maybe configured to generate a discrimination result between the images Band B′. The backward pipeline may be used to generate an image A′ (i.e.,a predicted image A) by warping the image B according to the image A.For example, the generator 1102B may predict a motion field 1103B fromthe image B to the image A of the training sample 1101. Thetransformation layer 1104B may generate the image A′ by warping theimage B according to the motion field 1103B. The discriminator 1105B maybe configured to generate a discrimination result between the images Aand A′.

In some embodiments, the preliminary model 1200 may be trained tominimize a loss function of the preliminary model 1200. The lossfunction of the preliminary model 1200 may include a first componentassociated with the forward pipeline and/or a second componentassociated with the backward pipeline. Each of the first component andthe second component may be similar to the loss function of thepreliminary model 1100 as described in connection with FIG. 11 . Takingthe forward pipeline as an instance, the corresponding first componentmay relate to a first difference and a discrimination result betweenimages B and B′ of each training sample, and optionally a consistencymotion loss.

In some embodiments, for the training sample 1101, the processing device140B may further replace the image A in the image pair (A, B) with theimage A′ generated by the back pipeline to generate an image pair (A′,B). The image pair (A′, B) may be inputted into the forward pipeline togenerate an image B″ (or also referred be to as a third image) bywarping the image A′ according to the image B of the training sample1101. In other words, the image A′ may be generated by performing abackward transformation on the image B of the training sample 1101, andthe image B″ may be generated by performing a forward transformation onthe image A′ of the training sample 1101. Theoretically, if thepreliminary model 1200 is accurate enough (e.g., having an accuracyhigher than a threshold), the image B″ may be substantially the same asthe image B of the training sample 1101. A third difference between theimages B and B″ may be determined and taken into consideration in thetraining of the preliminary model 1200. For example, the loss functionof the preliminary model 1200 may be determined based on the firstcomponent relating to the forward pipeline, the second componentrelating to the backward pipeline, the third difference of each trainingsample, or any combination thereof.

Additionally or alternatively, for the training sample 1101, theprocessing device 140B may replace the image B in the image pair (B, A)with the image B′ generated by the forward pipeline to generate an imagepair (B′, A). The image pair (B′, A) may be inputted in the backwardpipeline to generate an image A″ (or also referred be to as a fourthimage) by wrapping the image B′ according to the image A. Similar to theimages B and B″ as aforementioned, the images A and A″ may be supposedto be substantially the same as each other. A fourth difference betweenthe images A and A″ may be determined and taken into consideration inthe training of the preliminary model 1200. For example, the lossfunction of the preliminary model 1200 may be determined based on thefirst component, the second component, the third difference of eachtraining sample, the fourth difference of each training sample, or anycombination thereof.

In some embodiments, the training process of the preliminary model 1200may be an iterative process. An error may accumulate in the iterativeprocess, which may result in a drifting error and in turn a trainedmodel having a low accuracy. By taking into consideration of the thirddifference and/or the fourth difference of each training sample, thedrifting error may be eliminated or reduced, which, in turn, may improvethe accuracy and reliability of the second trained model.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, the process 600 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed above. For example, the process 600may include an additional operation to store the motion prediction modelin a storage device (e.g., the storage device 150, the storage 220,and/or the storage 390) for further use (e.g., in process 500).

In addition, the preliminary model exemplified above, such as thepreliminary models 1100 and 1200, is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. In some embodiments, one or more components of thepreliminary model may be omitted and/or the preliminary model mayinclude one or more additional components. For example, thediscriminator 1105A and/or the discriminator 1105B of the preliminarymodel 1200 may be removed. Additionally or alternatively, two or morecomponents of the preliminary model may be integrated into a singlecomponent. For example, the generator 1102A and the generator 11026 ofthe preliminary model 1200 may be integrated into a single generator.

In some embodiments, the generated motion prediction model may beutilized in image segmentation by the processing device 140B or anothercomputing device (e.g., the processing device 140A). For example, theprocessing device 140B may obtain an annotated image of a sample ROIcorresponding a third motion phase and unannotated image of the sampleROI corresponding a fourth motion phase. The third and fourth motionphases may be two different motion phases of the sample ROI. Theannotated image may include an annotation of one or more first featurepoints relating to the sample ROI. The identification of the firstfeature point(s) in the annotated image may be performed according to afeature point identification technique as described elsewhere in thisdisclosure (e.g., 502 and the relevant descriptions). The processingdevice 140B may determine a motion field of the first feature point(s)from the third motion phase to the fourth motion phase by inputting theannotated image and the unannotated image into the motion predictionmodel. The motion field of the first feature point(s) may be determinedin a similar manner as how the motion field of the feature point(s) ofthe reference image is determined as described in connection with 503.The processing device 140B may generate a second annotated image of thesample ROI corresponding the fourth motion phase based on the annotationof the first feature point(s) and the motion field of the first featurepoint(s). The second annotated image may include an annotation of one ormore second feature point(s) corresponding to the first featurepoint(s). For example, for a certain first feature point, the processingdevice 140B may determine the coordinate of a corresponding secondfeature point in the unannotated image by transforming the coordinate ofthe certain first feature point according to the motion vector of thecertain first feature point. The processing device 1406 may furthergenerate an annotation of the determined second feature pointcorresponding to the certain first feature point. In this way, the ROI(e.g., the second feature point(s) of the ROI) may be segmented in theunannotated image and a segmented image (i.e., the second annotatedimage) may be generated.

Optionally, the annotated image and the second annotated image may beused in the training of a segmentation model. The segmentation model maybe used to segment an ROI (e.g., the heart or a lung) from an imageincluding the ROI. The segmentation model may be trained by a computingdevice (e.g., the processing device 140A or 140B) of the imaging system100 or an external computing device. In some embodiments, thesegmentation model may be trained using a supervised learning technique.To this end, a plurality of training images labeled with a sample ROI(e.g., by a bounding box) may be needed. The labeling of the sample ROIin the training images may be time-consuming and inefficient. Theannotated image and the second annotated image may be used as anannotated training sample for training the segmentation model. This maymitigate the labeling effort, thus improving the training efficiency ofthe segmentation model.

In some embodiments, the trained model may include a traineddiscriminator, e.g., the one derived from the discriminator 1105. Thetrained discriminator may be utilized to evaluate the quality of thesecond annotated image. For example, the annotated image may be warpedbased on the motion field from the annotated image to the unannotatedimage. The warped annotated image and the unannotated image may beinputted into the trained discriminator, which may determine adiscrimination result between the warped annotated image and theunannotated image. Optionally, the discrimination result may include aprobability that the warped annotated image is a real image. Forexample, if the probability is greater than a threshold, it may suggestthat the motion field from the annotated image to the unannotated imageis reliable, which, in turn, may suggest that the quality of the secondannotated image generated based on the motion field is reliable andqualified for training of the segmentation model. If the probability issmaller than the threshold, the second annotated image may be discarded.

FIG. 7 is a flowchart illustrating an exemplary process for minimizing aloss function to generate a motion prediction model according to someembodiments of the present disclosure. In some embodiments, process 700may be executed by the imaging system 100. For example, the process 700may be implemented as a set of instructions (e.g., an application)stored in a storage device (e.g., the storage device 150, the storage220, and/or the storage 390). In some embodiments, the processing device140B (e.g., the processor 210 of the computing device 200, the CPU 340of the mobile device 300, and/or one or more modules illustrated in FIG.4B) may execute the set of instructions and may accordingly be directedto perform the process 700. Alternatively, the process 700 may beperformed by a computing device of a system of a vendor that providesand/or maintains such motion prediction model, wherein the system of thevendor is different from the imaging system 100. For illustrationpurposes, the following descriptions are described with reference to theimplementation of the process 700 by the processing device 140B, and notintended to limit the scope of the present disclosure.

As described in connection with FIG. 6 , in some embodiments, the motionprediction model may be generated by training a preliminary model usingone or more training samples. Each training sample may include a pair ofimages A and B corresponding to different motion phases of a sample ROI.In some embodiments, the preliminary model may include one or more modelparameters having one or more initial values before model training. Inthe training of the preliminary model, the value(s) of the modelparameter(s) of the preliminary model may be updated such that the lossfunction of the preliminary model may be minimized. In some embodiments,the training of the preliminary model may include one or moreiterations. For illustration purposes, a current iteration of theiteration(s) is described in the following description. The currentiteration may include one or more operations of process 700 illustratedin FIG. 7 .

In 701, for each training sample, the processing device 140B (e.g., themodel generation module 406, the processing circuits of the processor210) may generate a first motion field from the image A to the image Bof the training sample using an updated preliminary model determined ina previous iteration.

In 702, for each training sample, the processing device 140B (e.g., themodel generation module 406, the processing circuits of the processor210) may generate an image B′ by warping the image A of the trainingsample according to the first motion field using the updated preliminarymodel.

For example, the preliminary model may be the preliminary mode 1100 asillustrated in FIG. 11 or the preliminary model 1200 illustrated in FIG.12 . The updated preliminary models of the preliminary model 1100 andthe preliminary model 1200 determined in the previous iteration may bedenoted as a model M1 and a model M2, respectively, for the convenienceof description. The image pair (A, B) of a training sample may beinputted into the model M1 or the model M2 to obtain the image B′.

In 703, for each training sample, the processing device 140B (e.g., themodel generation module 406, the processing circuits of the processor210) may determine a first difference between the image B′ and the imageB of the training sample.

The first difference between the images B′ may be determined based on analgorithm for measuring a similarity degree or a difference between twoimages. More descriptions regarding the determination of the firstdifference of a training sample may be found elsewhere in the presentdisclosure. See, e.g., operation 602 and relevant descriptions thereof.

In 704, the processing device 140B (e.g., the model generation module406, the processing circuits of the processor 210) may determine a valueof the loss function based at least in part on the first differencecorresponding to each training sample.

In some embodiments, the training sample(s) may include a plurality oftraining samples. The loss function may be configured to measure anoverall level (e.g., an average value) of the first differences of thetraining samples. In some embodiments, the value of the loss functionmay be determined based on the first differences of the training samplesas well as one or more other metrics. Merely by way of example, the lossfunction of the preliminary model 1100 may incorporate a motionconsistency loss as described in connection with FIG. 6 . As anotherexample, the loss function of the preliminary model 1200 may beassociated with a third difference and/or a fourth difference of eachtraining sample as described in FIG. 6 .

In 705, the processing device 140B (e.g., the model generation module406, the processing circuits of the processor 210) may determine whetherthe value of the loss function is minimized in the current iteration.

For example, the value of the loss function may be regarded as beingminimized if the value of the loss function obtained in the currentiteration is less than a predetermined threshold. As another example,the value of the loss function may be regarded as being minimized if acertain count of iterations is performed, or the loss function convergessuch that the differences of the values of the loss function obtained inconsecutive iterations are within a threshold, etc.

In response to a determination that the value of the loss function isminimized, the process 700 may proceed to 706, in which the processingdevice 140B (e.g., the model generation module 406, the processingcircuits of the processor 210) may design at least a portion of theupdated preliminary model in the current iteration as the motionprediction model. For example, the updated generator derived from thegenerator 1102 in the model M1 may be regarded as a trained generatorand designated as the motion prediction model. As another example, theupdated generator derived from the generator 1102A or 1102B in the modelM2 may be regarded as a trained generator and designated as the motionprediction model.

In response to a determination that the value of the loss function isnot minimized in the current iteration, the process 700 may proceed 707,in which the processing device 140B (e.g., the model generation module406, the processing circuits of the processor 210) may further updatethe updated preliminary model to be used in a next iteration.

For example, the processing device 140B may update the value(s) of themodel parameter(s) of the updated preliminary model based on the valueof the loss function according to, for example, a backpropagationalgorithm. The processing device 140B may perform the next iterationuntil the value of the loss function is minimized. After the value ofthe loss function is minimized in a certain iteration, at least aportion of the updated preliminary model in the certain iteration may bedesignated as the motion prediction model.

It should be noted that the above descriptions regarding the process 700are merely provided for the purposes of illustration, and not intendedto limit the scope of the present disclosure. For persons havingordinary skills in the art, multiple variations and modifications may bemade under the teachings of the present disclosure. However, thosevariations and modifications do not depart from the scope of the presentdisclosure. In some embodiments, the order of the process 700 may not beintended to be limiting. Additionally or alternatively, the process 700may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed above. Forexample, the processing device 140B may further test the motionprediction model using a set of testing samples to determine whether atesting condition is satisfied. If the testing condition is notsatisfied, the process 700 may be performed again to further train thepreliminary model.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed object matter requires more features than areexpressly recited in each claim. Rather, inventive embodiments lie inless than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±1%, ±5%, ±10%, or ±20% variation of thevalue it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system, comprising: at least one storage deviceincluding a set of instructions for physiological motion measurement;and at least one processor configured to communicate with the at leastone storage device, wherein when executing the set of instructions, theat least one processor is configured to direct the system to performoperations including: acquiring a reference image of a region ofinterest (ROI) corresponding to a reference motion phase of the ROI anda target image of the ROI corresponding to a target motion phase of theROI, the target motion phase being different from the reference motionphase; identifying one or more feature points relating to the ROI fromthe reference image; determining, based on the reference image, a firstdistance between a first feature point and a second feature point of theone or more feature points, the first feature point and the secondfeature point being in the reference image corresponding to thereference motion phase; determining a motion field of the first featurepoint and the second feature point from the reference motion phase tothe target motion phase using a motion prediction model, wherein aninput of the motion prediction model includes at least the referenceimage and the target image; determining, based on the motion field, asecond distance between a third feature point in the target image,corresponding to the first feature point, and a fourth feature point inthe target image, corresponding to the second feature point; anddetermining, based on the first distance and the second distance, aphysiological condition of the ROI, wherein the motion prediction modelis generated by performing an iteration operation for training apreliminary model using at least one training sample, each of the atleast one training sample including a first sample image and a secondsample image indicative of a physiological motion of a sample region ofinterest (ROI), the first sample image corresponding to a first motionphase of the sample ROI, and the second sample image corresponding to asecond motion phase of the sample ROI, the iteration operating includingone or more iterations, at least one of the one or more iterationsincluding: for each of the at least one training sample, generating afirst motion field from the first sample image to the second sampleimage using an updated preliminary model determined in a previousiteration; generating a predicted second sample image according to thefirst motion field; and determining a first difference between thepredicted second sample image and the second sample image of thetraining sample; determining, based at least in part on the firstdifference corresponding to each of the at least one training sample, avalue of a loss function; and updating the updated preliminary model tobe used in a next iteration based on the value of the loss function. 2.The system of claim 1, wherein the ROI includes at least one of a heart,a lung, an abdomen, a chest, a stomach of a subject.
 3. The system ofclaim 1, wherein the ROI is a heart, the first feature point relating tothe heart in the reference image includes an inner point on anendocardium of the heart, and the second feature point relating to theheart in the reference image includes a corresponding outer point on anepicardium of the heart.
 4. The system of claim 3, wherein to identifythe inner point and the corresponding outer point from the referenceimage, the at least one processor is further configured to direct thesystem to perform additional operations including: segmenting, from thereference image, the endocardium and the epicardium; and identifying,based on positions of the endocardium and the epicardium, the innerpoint and the corresponding outer point from the reference image.
 5. Thesystem of claim 1, the ROI comprising a heart, wherein the motion fieldincludes a motion vector of the first feature point and a motion vectorof the second feature point, and to determine a physiological conditionof the heart, the at least one processor is further configured to directthe system to perform additional operations including: determining,based on the motion vector of the first feature point, the third featurepoint in the target image corresponding to the first feature point;determining, based on the motion vector of the second feature point, thefourth feature point in the target image corresponding to the secondfeature point; determining the second distance between the third featurepoint and the fourth feature point in the target motion phase; anddetermining, based on the first distance and the second distance, astrain value relating to the heart.
 6. The system of claim 1, whereinthe motion prediction model is trained according to an unsupervisedlearning technique.
 7. The system of claim 6, wherein the preliminarymodel is a generative adversarial network (GAN) model.
 8. The system ofclaim 1, wherein the motion prediction model is generated by minimizingthe loss function.
 9. The system of claim 1, wherein the predictedsecond sample image is generated by warping the first sample image ofthe training sample according to the first motion field.
 10. The systemof claim 1, wherein the at least one of the one or more iterationsfurther includes: for each of the at least one training sample,generating a second motion field from the second sample image to thefirst sample image using the preliminary model; determining an oppositemotion field of the second motion field; and determining a seconddifference between the opposite motion field and the first motion fieldof the training sample, wherein the value of the loss function isdetermined further based on the second difference corresponding to eachtraining sample.
 11. The system of claim 10, wherein the at least one ofthe one or more iterations further includes: for each of the at leastone training sample, generating a predicted first sample image bywarping the second sample image of the training sample according to thefirst sample image of the training sample using the preliminary model;generating a third sample image by warping the predicted first sampleimage according to the second sample image using the preliminary model;generating a fourth sample image by warping the predicted second sampleimage according to the first sample image using the preliminary model;and determining a third difference between the third sample image andthe second sample image and a fourth difference between the fourthsample image and the first sample image, wherein the value of the lossfunction is determined further based on the third difference and thefourth difference corresponding to each training sample.
 12. Anon-transitory computer readable medium, comprising a set ofinstructions for physiological motion measurement, wherein when executedby at least one processor, the set of instructions direct the at leastone processor to effectuate a method, the method comprising: acquiring areference image of a region of interest (ROI) corresponding to areference motion phase of the ROI and a target image of the ROIcorresponding to a target motion phase of the ROI, the target motionphase being different from the reference motion phase; identifying oneor more feature points relating to the ROI from the reference image;determining, based on the reference image, a first distance between afirst feature point and a second feature point of the one or morefeature points, the first feature point and the second feature pointbeing in the reference image corresponding to the in the referencemotion phase; determining a motion field of the first feature point andthe second feature point from the reference motion phase to the targetmotion phase using a motion prediction model, wherein an input of themotion prediction model includes at least the reference image and thetarget image; determining, based on the motion field, a second distancebetween a third feature point in the target image, corresponding to thefirst feature point, and a fourth feature point in the target image,corresponding to the second feature point; and determining, based on thefirst distance and the second distance, a physiological condition of theROI, wherein the motion prediction model is generated by performing aniteration operation for training a preliminary model using at least onetraining sample, each of the at least one training sample including afirst sample image and a second sample image indicative of aphysiological motion of a sample region of interest (ROI), the firstsample image corresponding to a first motion phase of the sample ROI,and the second sample image corresponding to a second motion phase ofthe sample ROI, the iteration operating including one or moreiterations, at least one of the one or more iterations including: foreach of the at least one training sample, generating a first motionfield from the first sample image to the second sample image using anupdated preliminary model determined in a previous iteration; generatinga predicted second sample image according to the first motion field; anddetermining a first difference between the predicted second sample imageand the second sample image of the training sample; determining, basedat least in part on the first difference corresponding to each of the atleast one training sample, a value of a loss function; and updating theupdated preliminary model to be used in a next iteration based on thevalue of the loss function.
 13. A method implemented on a computingdevice having at least one processor, and at least one storage device,the method comprising: acquiring a reference image of a region ofinterest (ROI) corresponding to a reference motion phase of the ROI anda target image of the ROI corresponding to a target motion phase of theROI, the target motion phase being different from the reference motionphase; identifying one or more feature points relating to the ROI fromthe reference image; determining, based on the reference image, a firstdistance between a first feature point and a second feature point of theone or more feature points, the first feature point and the secondfeature point being in the reference image corresponding to thereference motion phase; determining a motion field of the first featurepoint and the second feature point from the reference motion phase tothe target motion phase using a motion prediction model, wherein aninput of the motion prediction model includes at least the referenceimage and the target image; determining, based on the motion field, asecond distance between a third feature point in the target image,corresponding to the first feature point, and a fourth feature point inthe target image, corresponding to the second feature point; anddetermining, based on the first distance and the second distance, aphysiological condition of the ROI, wherein the motion prediction modelis generated by performing an iteration operation for training apreliminary model using at least one training sample, each of the atleast one training sample including a first sample image and a secondsample image indicative of a physiological motion of a sample region ofinterest (ROI), the first sample image corresponding to a first motionphase of the sample ROI, and the second sample image corresponding to asecond motion phase of the sample ROI, the iteration operating includingone or more iterations, at least one of the one or more iterationsincluding: for each of the at least one training sample, generating afirst motion field from the first sample image to the second sampleimage using an updated preliminary model determined in a previousiteration; generating a predicted second sample image according to thefirst motion field; and determining a first difference between thepredicted second sample image and the second sample image of thetraining sample; determining, based at least in part on the firstdifference corresponding to each of the at least one training sample, avalue of a loss function; and updating the updated preliminary model tobe used in a next iteration based on the value of the loss function. 14.The method of claim 13, wherein the ROI includes at least one of aheart, a lung, an abdomen, a chest, a stomach of a subject.
 15. Themethod of claim 13, wherein the ROI is a heart, the first feature pointrelating to the heart in the reference image includes an inner point onan endocardium of the heart, and the second feature point relating tothe heart in the reference image includes a corresponding outer point onan epicardium of the heart.
 16. The method of claim 15, wherein theidentifying the inner point and the corresponding outer point from thereference image includes: segmenting, from the reference image, theendocardium and the epicardium; and identifying, based on positions ofthe endocardium and the epicardium, the inner point and thecorresponding outer point from the reference image.
 17. The method ofclaim 13, the ROI comprising a heart, wherein the motion field includesa motion vector of the first feature point and a motion vector of thesecond feature point, and the determining a physiological condition ofthe heart includes: determining, based on the motion vector of the firstfeature point, the third feature point in the target image correspondingto the first feature point; determining, based on the motion vector ofthe second feature point, the fourth feature point in the target imagecorresponding to the second feature point; determining the seconddistance between the third feature point and the fourth feature point inthe target motion phase; and determining, based on the first distanceand the second distance, a strain value relating to the heart.
 18. Themethod of claim 13, wherein the motion prediction model is trainedaccording to an unsupervised learning technique.
 19. The method of claim18, wherein the preliminary model is a generative adversarial network(GAN) model.
 20. The method of claim 13, wherein the predicted secondsample image is generated by warping the first sample image of thetraining sample according to the first motion field.